Tutorial Questions | Week 6 COSC2779 – Deep Learning This tutorial is aimed at reviewing famous CNN architectures. Please try the questions before you join the session. 1. The GoogLeNet uses inception modules as the basic building block. (a) Why does inception module have multiple paths? (b) The following diagram shows a variant of the […]
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Deep Learning – COSC2779 – Introduction to Deep Learning Deep Learning – COSC2779 Introduction to Deep Learning Dr. Ruwan Tennakoon July 19, 2021 Lecture 1 (Part 1) Deep Learning – COSC2779 July 19, 2021 1 / 16 Outline 1 Introduction: Teaching Team 2 Course Overview 3 Introduction to Deep Learning 4 Review of Machine Learning
Lab Exercises | Week 1 COSC2779 – Deep Learning 1 Introduction In this weeks lab, we will explore deep learning infrastructure that will be used during the course. The main cloud based platforms we will be using are: • Google Colab • (optional) Kaggle Notebooks GPU The following text will provide an introduction on setting
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Deep Learning – COSC2779 – Regularization Deep Learning – COSC2779 Regularization Dr. Ruwan Tennakoon August 2, 2021 Reference: Chapter 7: Ian Goodfellow et. al., “Deep Learning”, MIT Press, 2016. Lecture 3 (Part 2) Deep Learning – COSC2779 August 2, 2021 1 / 13 Generalization Gap Image: Goodfellow, 2016. Simpler functions are more likely to generalize,
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COSC2779LabExercises_W2_3_solutions-checkpoint ¶ COSC 2779 | Deep Learning ¶ Week 1-2 Lab Exercises: **Introduction to Tensorflow** ¶ introduction¶ This lab is aimed at introducing the fundamentals of TensorFlow 2.0. The Lab is organized into six sub modules: Minimal example: 2D Linear regression. Exercise: Classifying hand written text MNIST. Advanced TensorFlow example: Understanding gradient tape and writing
Tutorial Questions | Week 4 COSC2779 – Deep Learning This tutorial is aimed at reviewing optimization techniques and regularization in deep learning. Please try the questions before you join the session. 1. Calculate the gradient update for the neural network below with given initialized weights, if the input data point is x = [1, 2]
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Tutorial Questions | Week 5 COSC2779 – Deep Learning This tutorial is aimed at reviewing convolutions in deep learning. Please try the questions before you join the session. 1. What is the output of the following convolution operation? 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 5
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Linear Regression Liang Zheng Australian National University liang. .au 1 Incremental Learning Z. Li and D. Hoiem. “Learning without forgetting.” TPAMI, 2017 Problem setting: A classifier is trained on classes 1,2,3; I have new classes 4,5,6,7; I want a new classifier that can recognize 1,2,…,7; In training the new classifier, we do not have access
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COSC2779LabExercises_W2_3 ¶ COSC 2779 | Deep Learning ¶ Week 1-2 Lab Exercises: **Introduction to Tensorflow** ¶ introduction¶ This lab is aimed at introducing the fundamentals of TensorFlow 2.0. The Lab is organized into six sub modules: Minimal example: 2D Linear regression. Exercise: Classifying hand written text MNIST. Advanced TensorFlow example: Understanding gradient tape and writing
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Tutorial Questions | Week 5 COSC2779 – Deep Learning This tutorial is aimed at reviewing convolutions in deep learning. Please try the questions before you join the session. 1. What is the output of the following convolution operation? 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 5
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